딥러닝 기술 및 응용 (Fall 2018, UST, 15984)

Home assignment 1의 수행에 필요한 precision, recall, accuracy, f1score 등의 metrics를 계산하기 위한 callback 함수의 사용법입니다.

callback 함수 선언부분과 콜부분, 결과 출력 부분만 포함되어 있습니다.

callback_metrics.py

LSTM 을 이용한 Text 의 multi-class classification 예제

뉴스 타이틀을 4개 분야로 분류

소스: https://www.kaggle.com/ngyptr/multi-class-classification-with-lstm

데이터 파일: https://www.kaggle.com/uciml/news-aggregator-dataset

# This Python 3 environment comes with many helpful analytics libraries installed

# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python

# For example, here's several helpful packages to load in 

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D

from keras.models import Sequential

from sklearn.feature_extraction.text import CountVectorizer

from keras.preprocessing.text import Tokenizer

from keras.preprocessing.sequence import pad_sequences

from sklearn.model_selection import train_test_split

from keras.utils.np_utils import to_categorical

from keras.callbacks import EarlyStopping

# Input data files are available in the "../input/" directory.

# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

import os

#print(os.listdir("../input"))

# Any results you write to the current directory are saved as output.

data = pd.read_csv('uci-news-aggregator.csv', usecols=['TITLE', 'CATEGORY'])

#M class has way less data than the orthers, thus the classes are unbalanced.

data.CATEGORY.value_counts()

#I do aspire here to have balanced classes

num_of_categories = 45000

shuffled = data.reindex(np.random.permutation(data.index))

e = shuffled[shuffled['CATEGORY'] == 'e'][:num_of_categories]

b = shuffled[shuffled['CATEGORY'] == 'b'][:num_of_categories]

t = shuffled[shuffled['CATEGORY'] == 't'][:num_of_categories]

m = shuffled[shuffled['CATEGORY'] == 'm'][:num_of_categories]

concated = pd.concat([e,b,t,m], ignore_index=True)

#Shuffle the dataset

concated = concated.reindex(np.random.permutation(concated.index))

concated['LABEL'] = 0

#One-hot encode the lab

concated.loc[concated['CATEGORY'] == 'e', 'LABEL'] = 0

concated.loc[concated['CATEGORY'] == 'b', 'LABEL'] = 1

concated.loc[concated['CATEGORY'] == 't', 'LABEL'] = 2

concated.loc[concated['CATEGORY'] == 'm', 'LABEL'] = 3

print(concated['LABEL'][:10])

labels = to_categorical(concated['LABEL'], num_classes=4)

print(labels[:10])

if 'CATEGORY' in concated.keys():

    concated.drop(['CATEGORY'], axis=1)

'''

 [1. 0. 0. 0.] e

 [0. 1. 0. 0.] b

 [0. 0. 1. 0.] t

 [0. 0. 0. 1.] m

'''

n_most_common_words = 8000

max_len = 130

tokenizer = Tokenizer(num_words=n_most_common_words, filters='!"#$%&()*+,-./:;<=>?@[\]^_`{|}~', lower=True)

tokenizer.fit_on_texts(concated['TITLE'].values)

sequences = tokenizer.texts_to_sequences(concated['TITLE'].values)

word_index = tokenizer.word_index

print('Found %s unique tokens.' % len(word_index))

X = pad_sequences(sequences, maxlen=max_len)

X_train, X_test, y_train, y_test = train_test_split(X , labels, test_size=0.25, random_state=42)

epochs = 10

emb_dim = 128

batch_size = 256

labels[:2]

print((X_train.shape, y_train.shape, X_test.shape, y_test.shape))

model = Sequential()

model.add(Embedding(n_most_common_words, emb_dim, input_length=X.shape[1]))

model.add(SpatialDropout1D(0.7))

model.add(LSTM(64, dropout=0.7, recurrent_dropout=0.7))

model.add(Dense(4, activation='softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])

print(model.summary())

history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size,validation_split=0.2,callbacks=[EarlyStopping(monitor='val_loss',patience=7, min_delta=0.0001)])

accr = model.evaluate(X_test,y_test)

print('Test set\n  Loss: {:0.3f}\n  Accuracy: {:0.3f}'.format(accr[0],accr[1]))

import matplotlib.pyplot as plt

acc = history.history['acc']

val_acc = history.history['val_acc']

loss = history.history['loss']

val_loss = history.history['val_loss']

epochs = range(1, len(acc) + 1)

plt.plot(epochs, acc, 'bo', label='Training acc')

plt.plot(epochs, val_acc, 'b', label='Validation acc')

plt.title('Training and validation accuracy')

plt.legend()

plt.figure()

plt.plot(epochs, loss, 'bo', label='Training loss')

plt.plot(epochs, val_loss, 'b', label='Validation loss')

plt.title('Training and validation loss')

plt.legend()

plt.show()

LSTM을 이용한 sequence (text data)의 binary classification 예제

# LSTM with dropout for sequence classification in the IMDB dataset

import numpy

from keras.datasets import imdb

from keras.models import Sequential

from keras.layers import Dense

from keras.layers import LSTM

from keras.layers.embeddings import Embedding

from keras.preprocessing import sequence

# fix random seed for reproducibility

numpy.random.seed(7)

# load the dataset but only keep the top n words, zero the rest

top_words = 5000

(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)

# truncate and pad input sequences

max_review_length = 500

X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)

X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)

# create the model

embedding_vecor_length = 32

model = Sequential()

model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))

model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))

model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

print(model.summary())

model.fit(X_train, y_train, epochs=3, batch_size=64)

# Final evaluation of the model

scores = model.evaluate(X_test, y_test, verbose=0)

print("Accuracy: %.2f%%" % (scores[1]*100))

오늘 진행된 주제 토론 자료입니다.

주제: Linear Classification

발표자: 최현진

1109_Linear Classifier_UST딥러닝기술및응용 주제발표_최현진.pdf

8주째 수업 슬라이드 입니다.

class08.pdf